neural_prophet
statsforecast
| neural_prophet | statsforecast | |
|---|---|---|
| 5 | 58 | |
| 3,635 | 3,565 | |
| - | 2.7% | |
| 8.6 | 8.9 | |
| 13 days ago | 5 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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neural_prophet
- Facebook Prophet: library for generating forecasts from any time series data
- Time series analysis of Bitcoin price in Python with fbprophet ?!
- 14 September 2021 - Daily Chat Thread
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[D] Stock prediction using lstm(plz help)
NeuralProphet
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Financial time-series data forecasting - any other tools besides Prophet?
Neural Prophet: https://github.com/ourownstory/neural_prophet
statsforecast
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TimeGPT-1
I can't find the TimeGPT-1 model.
LICENSE Apache-2
https://github.com/Nixtla/statsforecast/blob/main/LICENSE
Mentions ARIMA, ETS, CES, and Theta modeling
- Facebook Prophet: library for generating forecasts from any time series data
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Sales forecast for next two years
If you only have historical data: StatsForecast
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Time series and cross validation
I also recommend you check Nixtla's libraries, in particular StatsForecast and HierarchicalForecast. They offer a wide selection of forecasting models, and can work with multiple time series. Given that you're working with many products in a warehouse, I think the hierarchical forecast can be very useful, especially for the short time series (the ones that don't seem to have enough time stamps).
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Demand Planning
If you are mostly worried about time and use python you could try out Nixtla's statsforecast as it is very snappy. https://github.com/Nixtla/statsforecast
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Statistical vs Machine Learning vs Deep Learning Modeling for Time Series Forecasting
I was researching about using deep learning for time series forecasting applications when I came across two experiments by the Nixtla team. They showed that their traditional statistical ensemble (comprised of AutoARIMA, ETS, CES, and DynamicOptimizedTheta) beat a bunch of deep learning models (link) and also the AWS forecast API (link).
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Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/statsforecast/
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XGBoost for time series
Leaving these two repos here for anyone interested in trying decision tree regression or statistical forecasting baselines: - https://nixtla.github.io/mlforecast/ - https://github.com/Nixtla/statsforecast
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[Discussion] Amazon's AutoML vs. open source statistical methods
In this reproducible experiment, we compare Amazon Forecast and StatsForecast a python open-source library for statistical methods.
- Statistical methods outperform Amazon’s ML Forecast
What are some alternatives?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
scikit-hts - Hierarchical Time Series Forecasting with a familiar API
mlforecast - Scalable machine 🤖 learning for time series forecasting.
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
orbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
nixtla - Python SDK for TimeGPT, a foundational time series model
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
sysidentpy - A Python Package For System Identification Using NARMAX Models
fable - Tidy time series forecasting